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ReasonPatch
AI office hours that find the first break—not the answer
ReasonPatch is AI office hours for the exact step where a learner’s reasoning breaks. In protected live mode, GPT-5.6 Sol plans and synthesizes while three Luna probes independently test counterexamples, assumptions, and rubric evidence; failed roles fall back to Sol. The learner receives one Socratic question, revises their own work, and applies the idea to a fresh-context problem. The public launch is a credential-free guided replay with zero model calls—no keys or fabricated live traces.
Hey Product Hunt! I built ReasonPatch because AI tutors often answer before a learner has had the chance to repair their own reasoning.
The interaction is deliberately small: bring an attempt, preserve what works, locate the first consequential break, ask one Socratic question, revise the exact step, then apply the idea to a fresh problem. It currently covers formal logic, algebra, Python edge cases, and causal reasoning.
Under the hood, protected live mode uses GPT-5.6 Sol as planner and synthesizer, with three parallel Luna probes testing counterexamples, hidden assumptions, and rubric evidence. A failed Luna role falls back to Sol and is disclosed in the trace.
The public launch uses deterministic guided fixtures with zero model calls. That is intentional: anyone can inspect the learning loop without an API key, and the demo never pretends a recorded trace is live. It also makes no claims about grades, mastery, or learning outcomes before an educator study exists.
I’d especially value feedback from educators and learners: does one focused question create enough productive friction without becoming frustrating?
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About ReasonPatch on Product Hunt
“AI office hours that find the first break—not the answer”
ReasonPatch was submitted on Product Hunt and earned 3 upvotes and 1 comments, placing #150 on the daily leaderboard. ReasonPatch is AI office hours for the exact step where a learner’s reasoning breaks. In protected live mode, GPT-5.6 Sol plans and synthesizes while three Luna probes independently test counterexamples, assumptions, and rubric evidence; failed roles fall back to Sol. The learner receives one Socratic question, revises their own work, and applies the idea to a fresh-context problem. The public launch is a credential-free guided replay with zero model calls—no keys or fabricated live traces.
ReasonPatch was featured in Productivity (658.2k followers), Education (79k followers), Artificial Intelligence (475.9k followers) and GitHub (41.4k followers) on Product Hunt. Together, these topics include over 324.6k products, making this a competitive space to launch in.
Who hunted ReasonPatch?
ReasonPatch was hunted by Jesse P. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Jesse P